Prior-based patch-level representation learning for electric vehicle battery state-of-charge estimation across a wide temperature scope

Electric vehicles (EVs) powered by lithium-ion batteries have emerged as a global development trend. To ensure the safe and stable driving of EVs, it is imperative to address battery safety and thermal management issues, which rely heavily on the precise state-of-charge (SOC) estimation of the battery. However, estimating SOC under uncontrolled environmental temperatures remains an unresolved challenge. This study proposes a patch-level representation learning model based on domain knowledge to estimate the SOC over a wide temperature range. First, patches were adopted as inputs instead of traditional points, thereby mitigating error accumulation and capturing dynamic changes in the battery from these more informative representations. Second, the open-circuit voltage (OCV)-SOC-temperature relationship was incorporated to obtain the temperature-related SOC priors. Subsequently, the prior was updated recursively along the time dimension to obtain a more precise SOC estimate. The accuracy of the proposed model was confirmed experimentally for three driving cycles at six ambient temperatures, significantly reducing the root mean square error by 48.19% compared to popular existing models. Notably, the performance of the proposed method had an excellent improvement of 51.52% and 57.20% at -10 degrees C and -20 degrees C, respectively. Moreover, the parameter size of the proposed method was 39.748 KB, which significantly promoted the deployment and application of data-driven models in the real world.